How useful is Big Data in the fight against Obesity?

International experts came together in Cambridge just before Easter to explore how Big Data can be used to tackle obesity and obesity related diseases. The audience of 60 consisted of academic and non-academic experts, with wide ranging interests including big data, nutrition, health and geography.

The second of four meetings, this meeting focused on Data, Methods and Models. The morning session included presentations from network members followed by a panel discussion.

Presentations

Dr James Woodcock (CEDAR) & Dr Robin Lovelace (CDRC) discussed modelling and visualising large datasets to guide active policies. They presented a case study on the new Propensity to Cycle Tool, to demonstrate how novel forms of data and Big Data can serve public health through promotion of active travel.

Key points covered during the presentation included the issue that information needed for understanding travel behaviour and health are not at consistent geographies (Dr James Woodcock) and the need to join up public health/obesity research with training in coding, i.e. if you want to ‘do’ big data, you need to know how to code (Dr Robin Lovelace).

Dr Darren Greenwood from the University of Leeds covered interpreting results from analysis with Big Data and provided a number of interesting examples from epidemiology. He covered the common pitfalls and encouraged the audience to think about what makes data ‘big’.

Panel Discussion

Dr Seraphim Alvanides, Dr Daniel Lewis and Dr Sandy Tubeuf joined the morning’s speakers for a lively panel discussion before lunch, exploring the practicalities of using Big Data in the fight against obesity. Key points covered in the panel session include:

Issues around identification of subjects in data analysis with GPS coordinates discussed. Discussion included methods of analysis and the importance of masking location in code which is shared in addition to results which are disseminated. The importance of confidentiality and consent where applicable was highlighted.

There is chance of generating erroneous inferences from ‘big data’ if robust analysis methods are not used. Discussion focussed around whether it is better to be ‘roughly right’ rather than ‘precisely wrong’.

‘Big Data’ invites opportunity for ‘hypothesis free’ research. Discussion followed around the need for a priori hypotheses and whether ‘hypothesis free’ analysis is good research practice or just a ‘fishing expedition’.

Attendees took advantage of the winter sun and took a short walk around Cambridge, before ending the day with a networking lunch.

Dr Pablo Monsivais, who chaired the meeting commented on the success of the day: ‘I was pleased to see such a high level of interest and engagement from the audience, over half of which were not network members. We need fresh, joined-up approaches to tackling obesity and the talks and workshop illustrated how this strategic network is enabling important intellectual and methodological advances using big data.”